1. Field of the Invention
The present invention relates to the field of image processing, and more particularly, to a face recognition method robust to external illumination variation, using a facial image transformed by a Gabor filter as an input to a binary classifier to determine whether the input facial image is the same as a facial image stored in a database.
2. Description of the Related Art
With the advancement of information society, identification technology that can distinguish a person from other people is becoming increasingly important. A biometric is a measurement of any physical characteristic or personal trait of an individual that can be used to protect personal information or authenticate the identity of that individual using computer technology. Different forms of biometrics are well known and face recognition particularly provides several advantages, including identification technology in a non-contact manner, making it convenient and competitive, compared to other forms of biometrics such as fingerprint or iris scan, requiring individual's specific action or behavior. The facial recognition technology, which is an important component of multimedia database retrieval systems, is becoming more important as a variety of applications, including face-based motion video extraction, identification, Human Computer Interface (HCI) image retrieval, security, and monitoring systems.
However, the face recognition results are very sensitive to variations in internal factors such as identity, age, race, facial expression, and jewelry, and in external environment such as pose, illumination, and image process. In particular, since external illumination variations pose a significant challenge to face recognition algorithms, there is an urgent need to develop an algorithm that is robust to external illumination variations.
A representative example of face recognition method that is robust against external illumination changes is face recognition using Gabor filter (hereinafter referred to as ‘Gabor filtering’). The Gabor filtering is very robust to external illumination variations because it is based on mathematical modeling for response characteristics of a simple cell of a human eye. This technique that is also one of face recognition algorithms is now being used extensively in various applications.
Compared to face recognition methods such as Principal Component Analysis (PCA) using the overall configuration of a face, Gabor filtering, which is robust to external illumination variations, however, provides less accurate recognition result than binary classification based on statistical probability. The probability-based binary classification provides high accuracy in face recognition by minimizing experimental cumulative error and reducing the complexity of a hypothesis space.
Examples of binary classification include Support Vector Machines (SVM) and Nearest Neighbor (NN). SVM is more widely used in face recognition field. Binary classification is an approach that uses facial and non-facial training data sets to detect a facial region from an input image. A NN approach is used to classify an image into facial and non-facial regions by determining whether the result of NN classification is similar to that from a training data set for the existing facial image. While binary classification is used to decide whether an image belongs to a facial or non-facial region, it may be employed to verify the identity of a facial image by determining whether the facial image belongs to the same or different person.
SVM binary classification is a very promising face recognition technique in a field requiring recognition accuracy rather than speed. Since SVM has a significantly large amount of data to be processed compared to PCA or Gabor filtering, it may not be suitable for real-time face recognition or high-speed face recognition. However, this limitation is relaxed to some extent with the overwhelming improvements in microcomputer.
However, while binary classification exhibits excellent face recognition performance compared to other methods under general conditions, it provides significantly reduced performance under different illumination conditions. This is because binary classification still uses pixel intensity (gray level) as an input vector under changing illumination conditions. Preprocessing techniques such as histogram equalization and RetineX are used to overcome this problem but cannot offer a complete solution.